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dc.contributor.advisor1Santos, Alyson de Jesus dos Santos-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/5998752909180697pt_BR
dc.contributor.referee1Santos, Alyson de Jesus dos-
dc.contributor.referee1Latteshttp://lattes.cnpq.br/5998752909180697pt_BR
dc.contributor.referee2Santos, Lucèlia Cunha da Rocha-
dc.contributor.referee2Latteshttp://lattes.cnpq.br/2242046166554146pt_BR
dc.contributor.referee3Fialho, Michaella Socorro Bruce-
dc.contributor.referee3Latteshttp://lattes.cnpq.br/9348859124436505pt_BR
dc.creatorCavalcante, Vinícius Loureiro-
dc.date.accessioned2024-09-23T15:52:23Z-
dc.date.available2023-09-23-
dc.date.available2024-09-23T15:52:23Z-
dc.date.issued2023-12-22-
dc.identifier.citationCavalcante, Vinicius Loureiro. 71f. 2024. Uso de uma rede neural convolucional para detecção de covid-19 automática através de imagens de raio-x. Monografia (Engenharia de Controle e Automação) - Instituto Federal de Educação. Ciência e Tecnologia do Amazonas, Campus Manaus Distrito Industrial, Manaus, 2024.pt_BR
dc.identifier.urihttp://repositorio.ifam.edu.br/jspui/handle/4321/1512-
dc.description.abstractThis study aims to evaluate the effectiveness of using neural networks in the detection of COVID-19 through chest X-rays. Based on a literature review, the methodology for building the neural network will be defined, and it will be trained with data collected from reliable sources and analyzed to evaluate the accuracy of detection. The use of neural networks can be a promising and non-invasive alternative for the diagnosis of COVID-19, especially in regions where PCR tests are scarce or time-consuming. Additionally, the use of neural networks may offer advantages over other forms of diagnosis, such as computed tomography (CT), as chest radiographs are more widely available and less costly. However, it is important to consider the limitations and challenges encountered in using neural networks for this purpose, such as the lack of specificity in mild or asymptomatic cases and the need for quality equipment and trained professionals to interpret the images. This study aims to contribute to the advancement of COVID-19 diagnosis through non-invasive and effective methods, as well as to identify possible limitations and challenges in using neural networks for this purpose.pt_BR
dc.description.resumoEste trabalho tem como objetivo avaliar a eficácia do uso de redes neurais na detecção de COVID-19 por meio de radiografia de tórax. Com base em uma pesquisa bibliográfica, será definida a metodologia para a construção da rede neural, que será treinada com dados coletados de fontes confiáveis e analisados para avaliar a acurácia da detecção. A utilização de redes neurais pode ser uma alternativa promissora e não-invasiva para o diagnóstico de COVID-19, especialmente em regiões onde os testes de PCR são escassos ou demorados. Além disso, o uso de redes neurais pode oferecer vantagens em relação a outras formas de diagnóstico, como a tomografia computadorizada (TC), pois as radiografias de tórax são mais amplamente disponíveis e menos onerosas. No entanto, é importante considerar as limitações e desafios encontrados no uso de redes neurais para esse fim, como a falta de especificidade em casos leves ou assintomáticos e a necessidade de equipamentos de qualidade e profissionais treinados para interpretar as imagens. Com este estudo, espera-se contribuir para o avanço do diagnóstico de COVID-19 por meio de métodos não-invasivos e eficazes, além de identificar possíveis limitações e desafios no uso de redes neurais para esse fim.pt_BR
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dc.description.provenanceMade available in DSpace on 2024-09-23T15:52:23Z (GMT). No. of bitstreams: 1 USO DE UMA REDE NEURAL CONVOLUCIONAL PARA DETECÇÃO DE COVID-19 AUTOMÁTICA ATRAVÉS DE IMAGENS DE RAIO-X_CAVALCANTE_2023.pdf: 3006888 bytes, checksum: 83728a21d87766e5b9aa55258f3de91b (MD5) Previous issue date: 2023-12-22en
dc.languageporpt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentCampus Manaus Distritopt_BR
dc.publisher.initialsInstituto Federal do Amazonaspt_BR
dc.publisher.initialsIFAMpt_BR
dc.publisher.initialsEngenharia de Controle e Automaçãopt_BR
dc.publisher.initialsInstituto Federal do Amazonaspt_BR
dc.publisher.initialsIFAMpt_BR
dc.publisher.initialsEngenharia de Controle e Automaçãopt_BR
dc.publisher.initialsInstituto Federal do Amazonaspt_BR
dc.publisher.initialsIFAMpt_BR
dc.publisher.initialsEngenharia de Controle e Automaçãopt_BR
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Detecção de COVID-19 em Imagens de Raio-X de Tórax através de Seleção Automática de Pré-processamento e de Rede Neural Convolucional. SAIT, UNAIS; k v, Gokul Lal; Prajapati, Sunny; Bhaumik, Rahul; Kumar, Tarun; S, Sanjana; Bhalla, Kriti (2020), “Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays).”, Mendeley Data, V1, doi: 10.17632/9xkhgts2s6.1 M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and 82 Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-raypt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectRede neuralpt_BR
dc.subjectRadiografia de tóraxpt_BR
dc.subjectCOVID-19pt_BR
dc.subjectDiagnósticopt_BR
dc.subjectAcuráciapt_BR
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::ELETRONICA INDUSTRIAL, SISTEMAS E CONTROLES ELETRONICOSpt_BR
dc.titleUso de uma rede neural convolucional para detecção de covid-19 automática através de imagens de Raio-xpt_BR
dc.typeTrabalho de Conclusão de Cursopt_BR
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